Systems and methods for evaluating content

ABSTRACT

Systems, methods, and non-transitory computer-readable media can generate a saliency prediction model for identifying salient points of interest that appear during presentation of content items, provide at least one frame of a content item to the saliency prediction model, and obtain information describing at least a first salient point of interest that appears in the at least one frame from the saliency prediction model, wherein the first salient point of interest is predicted to be of interest to one or more users accessing the content item.

FIELD OF THE INVENTION

The present technology relates to the field of content provision. Moreparticularly, the present technology relates to techniques forevaluating content to be presented through computing devices.

BACKGROUND

Today, people often utilize computing devices (or systems) for a widevariety of purposes. Users can operate their computing devices to, forexample, interact with one another, create content, share content, andaccess information. Under conventional approaches, content items (e.g.,images, videos, audio files, etc.) can be made available through acontent sharing platform. Users can operate their computing devices toaccess the content items through the platform. Typically, the contentitems can be provided, or uploaded, by various entities including, forexample, content publishers and also users of the content sharingplatform. In some instances, the content items can be categorized and/orcurated.

SUMMARY

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured togenerate a saliency prediction model for identifying salient points ofinterest that appear during presentation of content items, provide atleast one frame of a content item to the saliency prediction model, andobtain information describing at least a first salient point of interestthat appears in the at least one frame from the saliency predictionmodel, wherein the first salient point of interest is predicted to be ofinterest to one or more users accessing the content item.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to obtain a set of training content items,generate respective aggregated heat map data for each of the trainingcontent items, wherein the aggregated heat map data for a trainingcontent item measures user view activity during presentation of thetraining content item, and train the saliency prediction model based atleast in part on the set of training content items and the respectiveaggregated heat map data.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to obtain respective view tracking datafor a set of users that accessed a training content item, wherein theview tracking data for a user identifies one or more regions that wereviewed in frames by the user during presentation of the training contentitem, generate user-specific heat map data for each user in the setbased at least in part on the respective view tracking data for theuser, and aggregate the user-specific heat map data to generate theaggregated heat map data for the training content item.

In some embodiments, the view tracking data for a user is determinedbased at least in part on changes to the user's viewport duringpresentation of the training content item.

In some embodiments, the changes to the viewport are determined based atleast in part on sensor data that describes movement of a computingdevice being used to present the training content item, gesture datathat describes gestures performed during presentation of the trainingcontent item, input device data that describes input operationsperformed during presentation of the training content item, headsetmovement data that describes changes in the viewport direction duringpresentation of the training content item, or eye tracking datacollected during presentation of the training content item.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine changes to a user's viewportduring presentation of the content item based at least in part on theinformation describing the first salient point of interest that appearsin the at least one frame.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to cause a first region that correspondsto the first salient point of interest in the at least one frame to beallocated a greater number of bits than a second region in the framethat does not correspond to a salient point of interest, wherein thefirst region is presented at a higher video quality than the secondregion during presentation of the content item.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to generate an automated guide for thecontent item, wherein the automated guide causes user viewports toautomatically transition between salient points of interest that appearduring presentation of the content item.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine a second content item basedat least in part on one or more salient points of interest that appearduring presentation of the content item and cause the second contentitem to be presented after presentation of the content item.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine a score for the first salientpoint of interest based at least in part on an amount of user viewactivity corresponding to the first salient point of interest, determinea score for a second salient point of interest that also appears in thecontent item based at least in part on an amount of user view activitycorresponding to the second salient point of interest, wherein the scorefor the first salient point of interest is greater than the score forthe second salient point of interest, and determine the second contentitem based at least in part on the first salient point of interest.

It should be appreciated that many other features, applications,embodiments, and/or variations of the disclosed technology will beapparent from the accompanying drawings and from the following detaileddescription. Additional and/or alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system including an example contentprovider module, according to an embodiment of the present disclosure.

FIG. 2 illustrates an example of a content features module, according toan embodiment of the present disclosure.

FIGS. 3A-D illustrate examples of streaming a virtual reality contentitem, according to an embodiment of the present disclosure.

FIG. 4 illustrates an example activity diagram, according to anembodiment of the present disclosure.

FIG. 5 illustrates an example method, according to an embodiment of thepresent disclosure.

FIG. 6 illustrates a network diagram of an example system including anexample social networking system that can be utilized in variousscenarios, according to an embodiment of the present disclosure.

FIG. 7 illustrates an example of a computer system or computing devicethat can be utilized in various scenarios, according to an embodiment ofthe present disclosure.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION

Approaches for Evaluating Content

People use computing devices (or systems) for a wide variety ofpurposes. As mentioned, under conventional approaches, a user canutilize a computing device to share content items (e.g., documents,images, videos, audio, etc.) with other users. Such content items can bemade available through a content sharing platform. Users can operatetheir computing devices to access the content items through theplatform. Typically, the content items can be provided, or uploaded, byvarious entities including, for example, content publishers and alsousers of the content sharing platform.

In some instances, a user can access virtual reality content through acontent provider. Such virtual reality content can be presented, forexample, in a viewport that is accessible through a computing device(e.g., a virtual reality device, headset, or any computing devicecapable of presenting virtual reality content). In general, a virtualreality content item (or immersive video) corresponds to any virtualreality media that encompasses (or surrounds) a viewer (or user). Someexamples of virtual reality content items include spherical videos, halfsphere videos (e.g., 180 degree videos), arbitrary partial spheres, 225degree videos, and 3D 360 videos. Such virtual reality content itemsneed not be limited to videos that are formatted using a spherical shapebut may also be applied to immersive videos formatted using other shapesincluding, for example, cubes, pyramids, and other shape representationsof a video recorded three-dimensional world. In some embodiments, avirtual reality content item can be created by stitching togethervarious video streams (or feeds) that were captured by cameras that areplaced at particular locations and/or positions to capture a view of thescene (e.g., 180 degree view, 225 degree view, 360 degree view, etc.).Once stitched together, a user can access, or present (e.g., playback),the virtual reality content item. Generally, while accessing the virtualreality content item, the user can zoom and change the direction (e.g.,pitch, yaw, roll) of the viewport to access different portions of thescene in the virtual reality content item. The direction of the viewportcan be used to determine which stream of the virtual reality contentitem is presented.

In general, a content item (e.g., virtual reality content item,immersive video, spherical video, etc.) may capture scenes that includevarious points of interest (e.g., persons, objects, landscapes, etc.).In some instances, conventional models (e.g., neural network) can betrained to evaluate the content item to identify points of interestappearing in scenes (e.g., frames) during presentation (e.g., playback)of the content item. Although conventional approaches can be used toidentify a number of different points of interest in a given contentitem, these approaches are typically unable to indicate which of theseidentified points of interest are likely to be relevant (or interesting)to a given user or a group of users. Accordingly, such conventionalapproaches may not be effective in addressing these and other problemsarising in computer technology.

An improved approach overcomes the foregoing and other disadvantagesassociated with conventional approaches. In various embodiments, asaliency prediction model can be trained to identify content that islikely to be of interest to users (e.g., salient points of interest)during presentation of a given content item. In some embodiments, thecontent predicted by the saliency prediction model is expected to bemore relevant, or interesting, to a given user or group of users (e.g.,users sharing one or more demographic attributes). In some embodiments,these salient points of interest can be used to improve the delivery (orstreaming) of the content item. For example, frames that include salientpoints of interest can be encoded so that a greater number of bits areallocated to regions in the frames that correspond to the salient pointsof interest. In this example, regions corresponding to salient points ofinterest would appear in higher quality over points of interest that mayappear in other regions of the frames. As a result, more resources canbe allocated to presenting content that is more likely to be viewed byusers during presentation of a content item.

FIG. 1 illustrates an example system 100 including an example contentprovider module 102, according to an embodiment of the presentdisclosure. As shown in the example of FIG. 1, the content providermodule 102 can include a content module 104, a streaming module 106, anda content features module 108. In some instances, the example system 100can include at least one data store 112. A client module 114 caninteract with the content provider module 102 over one or more networks150 (e.g., the Internet, a local area network, etc.). The client module114 can be implemented in a software application running on a computingdevice (e.g., a virtual reality device, headset, or any computing devicecapable of presenting virtual reality content). In various embodiments,the network 150 can be any wired or wireless computer network throughwhich devices can exchange data. For example, the network 150 can be apersonal area network, a local area network, or a wide area network, toname some examples. The components (e.g., modules, elements, etc.) shownin this figure and all figures herein are exemplary only, and otherimplementations may include additional, fewer, integrated, or differentcomponents. Some components may not be shown so as not to obscurerelevant details.

In some embodiments, the content provider module 102 can be implemented,in part or in whole, as software, hardware, or any combination thereof.In general, a module, as discussed herein, can be associated withsoftware, hardware, or any combination thereof. In some implementations,one or more functions, tasks, and/or operations of modules can becarried out or performed by software routines, software processes,hardware, and/or any combination thereof. In some cases, the contentprovider module 102 can be implemented, in part or in whole, as softwarerunning on one or more computing devices or systems, such as on a usercomputing device or client computing system. For example, the contentprovider module 102, or at least a portion thereof, can be implementedas or within an application (e.g., app), a program, or an applet, etc.,running on a user computing device or a client computing system, such asthe user device 610 of FIG. 6. Further, the content provider module 102,or at least a portion thereof, can be implemented using one or morecomputing devices or systems that include one or more servers, such asnetwork servers or cloud servers. In some instances, the contentprovider module 102 can, in part or in whole, be implemented within orconfigured to operate in conjunction with a social networking system (orservice), such as the social networking system 630 of FIG. 6. It shouldbe understood that there can be many variations or other possibilities.

In some embodiments, the content provider module 102 can be configuredto communicate and/or operate with the at least one data store 112 inthe example system 100. In various embodiments, the at least one datastore 112 can store data relevant to the function and operation of thecontent provider module 102. One example of such data can be contentitems (e.g., virtual reality content items) that are available foraccess (e.g., streaming). In some implementations, the at least one datastore 112 can store information associated with the social networkingsystem (e.g., the social networking system 630 of FIG. 6). Theinformation associated with the social networking system can includedata about users, social connections, social interactions, locations,geo-fenced areas, maps, places, events, pages, groups, posts,communications, content, feeds, account settings, privacy settings, asocial graph, and various other types of data. In some implementations,the at least one data store 112 can store information associated withusers, such as user identifiers, user information, profile information,user specified settings, content produced or posted by users, andvarious other types of user data. It should be appreciated that therecan be many variations or other possibilities.

In various embodiments, the content module 104 can provide access tovarious types of content items (e.g., virtual reality content items,immersive videos, etc.) to be presented through a viewport. Thisviewport may be provided through a display of a computing device (e.g.,a virtual reality computing device) in which the client module 114 isimplemented, for example. In some instances, the computing device may berunning a software application (e.g., social networking application)that is configured to present content items. Some examples of virtualreality content can include videos composed using monoscopic 360 degreeviews or videos composed using stereoscopic 180 degree views, to namesome examples. In various embodiments, virtual reality content items cancapture views (e.g., 180 degree views, 225 degree views, 360 degreeviews, etc.) of one or more scenes over some duration of time. Suchscenes may be captured from the real world and/or be computer generated.Further, a virtual reality content item can be created by stitchingtogether various video streams (or feeds) that were captured by camerasthat are placed at particular locations and/or positions to capture aview of the scene. Such streams may be pre-determined for variousdirections, e.g., angles (e.g., 0 degree, 30 degrees, 60 degrees, etc.),accessible in a virtual reality content item. Once stitched together, auser can access, or present, the virtual reality content item to view aportion of the virtual reality content item along some direction (orangle). Generally, the portion of the virtual reality content item(e.g., stream) shown to the user can be determined based on the locationand direction of the user's viewport in three-dimensional space. In someembodiments, a virtual reality content item (e.g., stream, immersivevideo, spherical video, etc.) may be composed using multiple contentitems. For example, a content item may be composed using a first contentitem (e.g., a first live broadcast) and a second content item (e.g., asecond live broadcast).

In one example, the computing device in which the client module 114 isimplemented can request presentation of a virtual reality content item(e.g., spherical video). In this example, the streaming module 106 canprovide one or more streams of the virtual reality content item to bepresented through the computing device. The stream(s) provided willtypically correspond to a direction of the viewport in the virtualreality content item being accessed. As presentation of the virtualreality content item progresses, the client module 114 can continuallyprovide the content provider module 102 with information describing thedirection at which the viewport is facing. The streaming module 106 canuse this information to determine which stream to provide the clientmodule 114. For example, while accessing the virtual reality contentitem, the client module 114 can notify the content provider module 102that the viewport is facing a first direction. Based on thisinformation, the streaming module 106 can provide the client module 114with a first stream of the virtual reality content item that correspondsto the first direction.

In some embodiments, the content features module 108 provides a numberof different features for enhancing the presentation of content items.For example, in some embodiments, the content features module 108 cangenerate a saliency prediction model that can be used to identifysalient points of interest in a given content item. The content featuresmodule 108 can use the identified salient points of interest to improvethe presentation of the content item. More details describing thecontent features module 108 will be provided below in reference to FIG.2.

FIG. 2 illustrates an example of a content features module 202,according to an embodiment of the present disclosure. In someembodiments, the content features module 108 of FIG. 1 can beimplemented with the content features module 202. As shown in theexample of FIG. 2, the content features module 202 can include atraining content module 204, a view tracking data module 206, a heat mapdata module 208, a saliency module 210, a head orientation predictionmodule 212, a bitrate allocation module 214, a content points ofinterest module 216, an auto-generated guide module 218, and anautomatic hotspot module 220.

In various embodiments, the training content module 204 can beconfigured to obtain content items to be used for training one or moremodels (e.g., saliency prediction models). Such content items mayinclude videos (e.g., virtual reality content items, immersive videos,etc.). In general, a virtual reality content item (or immersive video)corresponds to any virtual reality media that encompasses (or surrounds)a viewer (or user). Some examples of virtual reality content itemsinclude spherical videos, half sphere videos (e.g., 180 degree videos),arbitrary partial spheres, 225 degree videos, and 3D 360 videos. Suchvirtual reality content items need not be limited to videos that areformatted using a spherical shape but may also be applied to immersivevideos formatted using other shapes including, for example, cubes,pyramids, and other shape representations of a video recordedthree-dimensional world.

The content items obtained by the training content module 204 can varydepending on the type of model being trained. For example, in someembodiments, a general saliency prediction model may be trained usingvarious unrelated content items that were created by various publishersand corresponding heat map data for those content items. In someembodiments, such heat map data for a given content item may begenerated based on view tracking data for the content item, as describedbelow. This general saliency prediction model can be used to determinesalient points of interest in various types of content items. In someembodiments, a publisher-specific saliency prediction model may betrained using content items that were posted by a given publisher (e.g.,content creator) and corresponding heat map data for those contentitems. This publisher-specific saliency prediction model can be used todetermine salient points of interest in content that is subsequentlyposted by that publisher in which salient points of interest are notinitially known. In some embodiments, a category-specific saliencyprediction model may be trained using content items that all correspondto a given category (e.g., genre, topic, interest, etc.) andcorresponding heat map data for those content items. Thiscategory-specific saliency prediction model can be used to determinesalient points of interest in new content items that correspond to thegiven category.

In some embodiments, the view tracking data module 206 can be configuredto obtain respective view tracking data for each of the content itemsbeing used to train the models. For example, view tracking data for agiven content item may be collected for each user (or viewer) that hasaccessed the content item. The view tracking data for a user mayidentify regions that were accessed through the user's viewport duringpresentation of the content item. Such view tracking data may becollected for each frame corresponding to the content item. In someembodiments, a user's view tracking data for a content item can bedetermined based on changes to the user's viewport during presentationof the content item. Such changes to the viewport may be measured usingvarious approaches that can be used either alone or in combination. Forexample, changes to the viewport may be measured using sensor data(e.g., gyroscope data, inertial measurement unit data, etc.) thatdescribes movement of the computing device being used to present thecontent item. In another example, changes to the viewport can bemeasured using gesture data describing the types of gestures (e.g.,panning, zooming, etc.) that were performed during presentation of thecontent item. Some other examples for measuring changes to the viewportinclude using input device data that describes input operations (e.g.,mouse movement, dragging, etc.) performed during presentation of thecontent item, headset movement data that describes changes in theviewport direction during presentation of the content item, and eyetracking data collected during presentation of the content item, to namesome examples.

In some embodiments, the heat map data module 208 can be configured togenerate (or obtain) heat maps for each of the content items being usedto train the models. In some embodiments, heat maps for a given contentitem may be generated based on view tracking data for the content item.As mentioned, the view tracking data module 206 can obtain respectiveview tracking data for users that viewed a content item. Each user'sview tracking data can indicate which regions of a given frame (or setof frames) were accessed using a user's viewport during presentation ofa content item. That is, for any given frame in the content item, theheat map data module 208 can generate (or obtain) user-specific heatmaps that graphically represent regions in the frame that were ofinterest to a given user. In some embodiments, heat maps can begenerated for a set of frames that correspond to some interval of time.For example, a respective heat map can be generated for every second ofthe content item. In some embodiments, user-specific heat maps for agiven content item can be combined to generate aggregated heat maps thatrepresent aggregated regions of interest in frames corresponding to thecontent item. Thus, for example, the respective user-specific heat mapscan be aggregated on a frame-by-frame basis so that each frame of thecontent item is associated with its own aggregated heat map thatidentifies the regions of interest in the frame. These regions ofinterest can correspond to various points of interest that appear inframes and were determined to be of interest to some, or all, of theusers that viewed the content item. In some embodiments, these regionsof interest can correspond to various points of interest that appear inframes and were determined to be of interest to users sharing one ormore common characteristics with the user who is to view the contentitem.

In some embodiments, the saliency module 210 can be configured to traina saliency prediction model. In such embodiments, the saliencyprediction model can be used to identify content (e.g., points ofinterest) that is likely to be of interest to a given user accessing acontent item in which the identified content appears. For example, thesaliency prediction model can determine that a first point of interestwhich appears in a given frame of a content item is likely to be ofinterest to a user over a second point of interest that also appears inthe frame. In some embodiments, the saliency prediction model is trainedusing the content items that were obtained by the training contentmodule 204 and their respective aggregated heat maps. For example, insome embodiments, each frame of a content item and its correspondingaggregated heat map can be provided as a training example to thesaliency prediction model. In some embodiments, the saliency predictionmodel is trained using aggregated heat map data that has been labeled toidentify points of interest. The aggregated heat map can be used toidentify regions of the frame that were viewed more than others. Suchview activity can be represented in the aggregated heat map usingvarious shapes that describe the size of the view region and/or colorsthat indicate concentrations of view activity in any given region of theframe. Based on this information, the saliency prediction model canlearn which pixels in the frame were interesting (or relevant) to usersin the aggregate. In some embodiments, pixels in the frame that fallwithin the shapes and/or colors represented in the aggregated heat mapcan be identified as being interesting (or relevant) to users in theaggregate. In some embodiments, these pixels correlate to points ofinterest that appear in frames. As a result, the saliency predictionmodel can learn which points of interest appearing in a frame were ofinterest to users in the aggregate with respect to other points ofinterest that also appear in the frame. Once trained, the saliencyprediction model can be used to identify content (e.g., points ofinterest) that is likely to be of interest in new content items. In someembodiments, the saliency prediction model can be used to predictsalient points of interest for stored content items (e.g., videoon-demand). In some embodiments, the saliency prediction model can beused to predict salient points of interest (e.g., points of interestthat are likely to be of interest) for live content items (e.g., livevideo broadcasts).

In various embodiments, heat map data, aggregated or otherwise, need notbe actual heat maps that are represented graphically but may instead besome representation of view tracking data. For example, in someembodiments, the heat map data may identify clusters of view activitywithin individual frames of content items. In some embodiments, theclusters of view activity that are identified from heat map data can beused independently to identify salient points of interest in variouscontent items. For example, in some embodiments, heat map dataidentifying clusters of view activity in frames during a live videobroadcast (e.g., over the past n seconds of the broadcast) can be usedto identify salient points of interest that appear in subsequent frames.

The ability to predict salient content (e.g., points of interest) in newcontent items provides a number of advantages. For example, in someembodiments, the head orientation prediction module 212 can beconfigured to determine changes to a user's head orientation duringpresentation of a given content item. In such embodiments, the contentitem (or frames of the content item) being viewed can be provided asinput to the saliency prediction model that was trained by the saliencymodule 210. The saliency prediction model can output informationindicating which content in the frames is likely to be of interest tothe user viewing the content item. In general, the user's headorientation (e.g., viewport) is expected to align with regions in theframes that include content that is likely to be of interest. In someembodiments, predicted changes to the user's head orientation can beused to improve streaming of the content item. For example, in someembodiments, the predicted changes to the user's head orientation can beused to improve view dependent streaming of the content item. In someembodiments, the predicted changes to the user's head orientation can beused to improve dynamic streaming of the content item. For example, insome embodiments, rather than generating all of the possible viewportsfor a content item, which may include content that is not expected to beviewed by a user, only viewports that correspond to the predicteddirections of the user's head orientation during presentation of acontent item can be generated.

In some embodiments, the bitrate allocation module 214 can be configuredto allocate more bits (or macroblocks) to regions in a frame thatinclude content (e.g., points of interest) that is determined to be ofinterest. The additional allocation of bits to a given region in a frameallows that region to be presented in higher quality over other regionsin the frame. In some embodiments, such content can be determined usinga saliency prediction model as described above. For example, the contentitem (or frames of the content item) being viewed can be provided asinput to the saliency prediction model. The saliency prediction modelcan output information indicating which content in the frames is likelyto be of interest to the user viewing the content item. The regions inthe frame that correspond to content that is likely to be of interestcan be allocated a greater number of bits over other regions in theframe. The actual number of bits allocated among the various regions ofa frame can vary depending on the implementation. As a result, a user'sdata usage while streaming the content item can be throttled whileallowing more bits to be allocated for the more interesting regions inthe frames.

In some embodiments, the content points of interest module 216 can beconfigured to score content (e.g., points of interest) that appears inframes of a content item. For example, in some embodiments, content in aframe can be scored based on an aggregated heat map that reflects userview activity for the frame. As mentioned, an aggregated heat map for aframe can identify the respective amounts of view activity correspondingto various regions (or points of interest) in the frame. Such viewactivity can be represented in the aggregated heat map using variousshapes that describe the size of the view region and/or colors thatindicate concentrations of view activity in any given region of theframe. In some embodiments, content (e.g., points of interest) in theframe is scored with respect to the shapes and/or colors represented inthe aggregated heat map. For example, content (e.g., points of interest,salient points of interest, etc.) that appears in a region having athreshold concentration of view activity, as measured by the aggregatedheat map, can be scored higher than content in other regions thatreceived less view activity.

In some embodiments, points of interest that have been scored for framesof a content item can be used to provide various features. For example,in some embodiments, the auto-generated guide module 218 can beconfigured to generate a guide that automatically transitions userviewports during presentation of a given content item. For example, theguide for a content item can be generated based on the respective scoresof points of interest that appear in the content item as determined bythe content points of interest module 216. In some embodiments, pointsof interest that satisfy a threshold score can be selected for theguide. The auto-generated guide module 218 can determine a viewporttrajectory that will automatically transition a user's viewport duringpresentation of the content item from one selected point of interest toanother. In some instances, there may not be any heat map data availablefor a content item. For example, no heat map data may be available for arecently uploaded content item. In such instances, the auto-generatedguide module 218 can determine salient points of interest that appear inthe content item using the saliency prediction model, as describedabove. In some embodiments, the auto-generated guide module 218 usesthese salient points of interest to generate a guide that automaticallytransitions user viewports between the points of interest duringpresentation of the content item. In some embodiments, general objectdetection techniques (e.g., a trained object classifier) can be appliedto identify points of interest that appear in the content item. Theauto-generated guide module 218 can use these detected points ofinterest to generate the guide for the content item.

When transitioning between points of interest, the auto-generated guidemodule 218 may apply one or more different film transitioningtechniques. In one example, the transitioning may be performed using adissolve effect in which the transition between scenes and/or points ofinterest is gradual. In another example, the transitioning may beperformed using a cut effect. Other examples include a wipe transitioneffect, linear transitioning, easing, and hinting (e.g., using adirectional indicator before performing a transition). In someembodiments, the viewport is not automatically transitioned when theguide is enabled. Instead, a point of interest may be visually indicatedin the viewport using a directional indicator (e.g., arrow) that pointsto the position or direction of the point of interest. In suchembodiments, the user has the option to manually maneuver the viewportto correspond to the point of interest. In some embodiments, theauto-generated guide module 218 may apply different film transitioningtechniques depending on the type of device being used (e.g., mobilecomputing device, a virtual reality system, a head mounted display,etc.). For example, viewport transitions may be automatic in mobilecomputing devices but not in virtual reality systems and/or head mounteddisplays. The device type may also affect how the viewport istransitioned between scenes and/or points of interest as well as whichtransition effects are used. For example, the directional indicatorand/or dissolve effect may be used to perform the viewport transitionswhen the device is a virtual reality head mounted display. In anotherexample, transition effects may be disabled when performing viewporttransitions when the device is a mobile computing device.

As mentioned, the guide generated by the auto-generated guide module 218can automatically transition the viewport between points of interestwhile accessing a given content item. In some embodiments, oncepresentation of a first content item ends, the automatic hotspot module220 can automatically begin presentation of a different second contentitem. In some embodiments, the second content item can be identifiedbased on one or more points of interest that appear in the first contentitem. For example, if a particular singer appears during presentation ofthe first content item, then the automatic hotspot module 220 canidentify another content item (e.g., the second content item) in whichthe same singer appears. There may be instances in which many points ofinterest appear during presentation of the first content item. In someembodiments, the automatic hotspot module 220 selects the best scoringpoint of interest in the first content item and identifies relatedcontent items in which the best scoring point of interest appears. Moredetails describing approaches for automatically generating guides forcontent items are described in U.S. patent application Ser. No.15/144,695, filed May 2, 2016, entitled “Systems and Methods forPresenting Content”, which is incorporated by reference herein.

FIG. 3A-D illustrate examples of streaming a virtual reality contentitem, according to an embodiment of the present disclosure. FIG. 3Aillustrates an example 300 of a viewport 304 displaying a portion of avideo stream 306 of a spherical video. The viewport 304 is shown in thediagram of FIG. 3A as being positioned within a representation 302 of aspherical video to facilitate understanding of the various embodimentsdescribed herein. In some embodiments, a spherical video captures a360-degree view of a scene (e.g., a three-dimensional scene). Thespherical video can be created by stitching together various videostreams, or feeds, that were captured by cameras positioned atparticular locations and/or positions to capture a 360 degree view ofthe scene. FIGS. 3A-D refer to spherical videos as just one exampleapplication of the various technology described herein. Depending on theimplementation, such technology can be applied to other types of videosapart from spherical videos.

Once stitched together, a user can access, or present, the sphericalvideo through a viewport 304 to view a portion of the spherical video atsome angle. The viewport 304 may be accessed through a softwareapplication (e.g., video player software) running on a computing device.The stitched spherical video can be projected as a sphere, asillustrated by the representation 302. Generally, while accessing thespherical video, the user can change the direction (e.g., pitch, yaw,roll) of the viewport 304 to access another portion of the scenecaptured by the spherical video. FIG. 3B illustrates an example 350 inwhich the direction of the viewport 354 has changed in an upwarddirection (as compared to viewport 304). As a result, the video stream356 of the spherical video being accessed through the viewport 354 hasbeen updated (e.g., as compared to video stream 306) to show the portionof the spherical video that corresponds to the updated viewportdirection.

The direction of the viewport 304 may be changed in various waysdepending on the implementation. For example, while accessing thespherical video, the user may change the direction of the viewport 304using a mouse or similar device or through a gesture recognized by thecomputing device. As the direction changes, the viewport 304 can beprovided a stream corresponding to that direction, for example, from acontent provider system. In another example, while accessing thespherical video through a display screen of a mobile device, the usermay change the direction of the viewport 304 by changing the direction(e.g., pitch, yaw, roll) of the mobile device as determined, forexample, using gyroscopes, accelerometers, touch sensors, and/orinertial measurement units in the mobile device. Further, if accessingthe spherical video through a virtual reality head mounted display, theuser may change the direction of the viewport 304 by changing thedirection of the user's head (e.g., pitch, yaw, roll). Naturally, otherapproaches may be utilized for navigating presentation of a sphericalvideo including, for example, touch screen or other suitable gestures.

In some embodiments, the stream(s) are provided in real-time based onthe determined direction of the viewport 304. For example, when thedirection of the viewport 304 changes to a new position, the computingdevice through which the viewport 304 is being accessed and/or thecontent provider system can determine the new position of the viewport304 and the content provider system can send, to the computing device,stream data corresponding to the new position. Thus, in suchembodiments, each change in the viewport 304 position is monitored, inreal-time (e.g., constantly or at specified time intervals) andinformation associated with the change is provided to the contentprovider system such that the content provider system may send theappropriate stream that corresponds to the change in direction. Invarious embodiments, changes in the direction of the viewport 304 duringpresentation of the content item are captured and stored. In someembodiments, such viewport tracking data is used to generate one or moreuser-specific heat maps and/or aggregated heat maps for the contentitem. For example, FIG. 3C illustrates an example user-specific heat map360 that was generated based on changes to the user's viewport direction(e.g., view activity) during presentation of the video. In the exampleof FIG. 3C, the user-specific heat map 360 indicates that the user'sattention was focused on a first point of interest 362 and a secondpoint of interest 364 during presentation of the spherical video. Thisheat map data can be used for myriad applications as described above.For example, in some embodiments, such user-specific heat maps can beaggregated and used to train a saliency prediction model, as describedabove. The saliency prediction model can be used to determine salientpoints of interest in various content items. For example, FIG. 3Dillustrates an example frame 370 of a content item which includes afirst point of interest 372 and a second point of interest 374. Theframe 370 can be provided to the saliency prediction model to determinesalient points of interest. In this example, the saliency predictionmodel may determine that the second point of interest 374 is a salientpoint of interest that is likely to be of interest to users viewing thecontent item. In some embodiments, the second point of interest 374 (ora region 376 corresponding to the second point of interest 374) can beenhanced visually during presentation of the content item, as describedabove. In some embodiments, the region 376 can correspond to thecontours of the second point of interest 374.

FIG. 4 illustrates an example activity diagram 400, according to anembodiment of the present disclosure. It should be appreciated thatthere can be additional, fewer, or alternative steps performed insimilar or alternative orders, or in parallel, within the scope of thevarious embodiments discussed herein unless otherwise stated.

At block 402, content items to be used for training one or more models(e.g., a saliency prediction model) are obtained. As mentioned, suchcontent items may include videos (e.g., virtual reality content items,immersive videos, etc.). In general, a virtual reality content item (orimmersive video) corresponds to any virtual reality media thatencompasses (or surrounds) a viewer (or user). Some examples of virtualreality content items include spherical videos, half sphere videos(e.g., 180 degree videos), arbitrary partial spheres, 225 degree videos,and 3D 360 videos. Such immersive videos need not be limited to videosthat are formatted using a spherical shape but may also be applied toimmersive videos formatted using other shapes including, for example,cubes, pyramids, and other shape representations of a video recordedthree-dimensional world.

At block 404, respective view tracking data for each of the contentitems being used to train the models is obtained. For example, viewtracking data for a given content item may be collected for each user(or viewer) that accessed the content item. The view tracking data for auser may identify regions that were accessed through the user's viewportduring presentation of the content item. Such view tracking data may becollected for each frame corresponding to the content item. In someembodiments, heat maps for a given content item may be generated basedon view tracking data for the content item, as described above. Forexample, user-specific heat maps can be generated for a given contentitem based on the respective view activity of those users. In someembodiments, such user-specific heat maps can be combined to generate anaggregated heat map for the content item. This aggregated heat map canindicate which regions in a given frame of the content item were mostpopular, or interesting, to users that viewed the content item.

At block 406, various approaches for analyzing user view activity can beapplied to determine (or predict) user-specific points of interest in agiven content item. For example, in some embodiments, a user-specificsaliency model can be trained using content items viewed by the user andrespective user-specific heat map data. As mentioned, in someembodiments, the heat map data can identify regions that were ofinterest to the user in a given content item on a frame-by-frame basis.This heat map data can be determined based on the user's interactionswith content items during presentation (e.g., sensor data, gesture data,input device data, headset movement data, eye tracking data, etc.) asdescribed above. In some embodiments, the user-specific model can beused to predict which points of interest are likely to be of interest tothe user in other content items accessed by the user. Such predictionsmay be made for stored content items (e.g., video on-demand) and livecontent items (e.g., live video broadcasts). In some embodiments, thevideo quality of salient points of interest can be enhanced, forexample, by allocating bits (or macroblocks) to regions in frames thatcorrespond to such points of interest.

In some embodiments, one or more frames of a given content item canautomatically be extracted to create additional content. For example, aportion of content (e.g., one or more frames) that has been determinedto be of interest can be extracted from the content item, for example,as one or more images or a short video. In some embodiments, suchportions of interesting content can be identified based on userinteractions during presentation of the content item. Such userinteractions may be measured using sensor data, gesture data, inputdevice data, headset movement data, eye tracking data, to name someexamples. More details describing approaches for automaticallyextracting content are described in U.S. patent application Ser. No.15/144,695, filed May 2, 2016, entitled “Systems and Methods forPresenting Content”, which is incorporated by reference herein.

At block 408, one or more saliency prediction models can be generated.For example, in some embodiments, a general saliency prediction modelcan be trained using aggregated heat maps that describe user viewtracking data for various content items, as described above. In someembodiments, this saliency prediction model can be used to predictcontent (e.g., points of interest) that is likely to be of interest tousers during presentation of a content item.

At block 410, content (e.g., points of interest) predicted to be ofinterest by the saliency prediction model can be used to determinechanges to a user's head orientation during presentation of a givencontent item. For example, the content item (or frames of the contentitem) being viewed by the user can be provided as input to the saliencyprediction model. The saliency prediction model can output informationindicating which content in the frames is likely to be of interest tothe user viewing the content item. In general, the user's headorientation (e.g., viewport) is expected to align with regions in theframes that include content that is likely to be of interest. Such headorientation predictions can be used to improve the delivery of contentto users, as described above.

At block 412, bitrate allocation techniques can be applied to improvethe quality in which the content predicted to be of interest ispresented. For example, additional bits can be allocated to regions inframes that include content (e.g., points of interest) likely to be ofinterest over other regions in the frames, as described above.

At block 414, content (e.g., points of interest) that appears in framesof a content item can be scored. In some embodiments, content in theframe is scored with respect to the shapes and/or colors represented inan aggregated heat map for the content item, as described above.

At block 416, points of interest that have been scored for frames of acontent item can be used to generate a guide that automaticallytransitions user viewports during presentation of a given content item,as described above.

At block 418, at least one other content item to be presented afterpresentation of a given content can be identified. For example, in someembodiments, the other content item can be identified based on one ormore points of interest that appear in the given content item, asdescribed above.

FIG. 5 illustrates an example method 500, according to an embodiment ofthe present disclosure. It should be appreciated that there can beadditional, fewer, or alternative steps performed in similar oralternative orders, or in parallel, within the scope of the variousembodiments discussed herein unless otherwise stated.

At block 502, a saliency prediction model for identifying salient pointsof interest that appear during presentation of content items isgenerated. At block 504, at least one frame of a content item isprovided to the saliency prediction model. At block 506, informationdescribing at least a first salient point of interest that appears inthe at least one frame is obtained from the saliency prediction model.The first salient point of interest is predicted to be of interest toone or more users accessing the content item.

It is contemplated that there can be many other uses, applications,and/or variations associated with the various embodiments of the presentdisclosure. For example, in some cases, user can choose whether or notto opt-in to utilize the disclosed technology. The disclosed technologycan also ensure that various privacy settings and preferences aremaintained and can prevent private information from being divulged. Inanother example, various embodiments of the present disclosure canlearn, improve, and/or be refined over time.

Social Networking System—Example Implementation

FIG. 6 illustrates a network diagram of an example system 600 that canbe utilized in various scenarios, in accordance with an embodiment ofthe present disclosure. The system 600 includes one or more user devices610, one or more external systems 620, a social networking system (orservice) 630, and a network 650. In an embodiment, the social networkingservice, provider, and/or system discussed in connection with theembodiments described above may be implemented as the social networkingsystem 630. For purposes of illustration, the embodiment of the system600, shown by FIG. 6, includes a single external system 620 and a singleuser device 610. However, in other embodiments, the system 600 mayinclude more user devices 610 and/or more external systems 620. Incertain embodiments, the social networking system 630 is operated by asocial network provider, whereas the external systems 620 are separatefrom the social networking system 630 in that they may be operated bydifferent entities. In various embodiments, however, the socialnetworking system 630 and the external systems 620 operate inconjunction to provide social networking services to users (or members)of the social networking system 630. In this sense, the socialnetworking system 630 provides a platform or backbone, which othersystems, such as external systems 620, may use to provide socialnetworking services and functionalities to users across the Internet.

The user device 610 comprises one or more computing devices (or systems)that can receive input from a user and transmit and receive data via thenetwork 650. In one embodiment, the user device 610 is a conventionalcomputer system executing, for example, a Microsoft Windows compatibleoperating system (OS), Apple OS X, and/or a Linux distribution. Inanother embodiment, the user device 610 can be a computing device or adevice having computer functionality, such as a smart-phone, a tablet, apersonal digital assistant (PDA), a mobile telephone, a laptop computer,a wearable device (e.g., a pair of glasses, a watch, a bracelet, etc.),a camera, an appliance, etc. The user device 610 is configured tocommunicate via the network 650. The user device 610 can execute anapplication, for example, a browser application that allows a user ofthe user device 610 to interact with the social networking system 630.In another embodiment, the user device 610 interacts with the socialnetworking system 630 through an application programming interface (API)provided by the native operating system of the user device 610, such asiOS and ANDROID. The user device 610 is configured to communicate withthe external system 620 and the social networking system 630 via thenetwork 650, which may comprise any combination of local area and/orwide area networks, using wired and/or wireless communication systems.

In one embodiment, the network 650 uses standard communicationstechnologies and protocols. Thus, the network 650 can include linksusing technologies such as Ethernet, 802.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriberline (DSL), etc. Similarly, the networking protocols used on the network650 can include multiprotocol label switching (MPLS), transmissioncontrol protocol/Internet protocol (TCP/IP), User Datagram Protocol(UDP), hypertext transport protocol (HTTP), simple mail transferprotocol (SMTP), file transfer protocol (FTP), and the like. The dataexchanged over the network 650 can be represented using technologiesand/or formats including hypertext markup language (HTML) and extensiblemarkup language (XML). In addition, all or some links can be encryptedusing conventional encryption technologies such as secure sockets layer(SSL), transport layer security (TLS), and Internet Protocol security(IPsec).

In one embodiment, the user device 610 may display content from theexternal system 620 and/or from the social networking system 630 byprocessing a markup language document 614 received from the externalsystem 620 and from the social networking system 630 using a browserapplication 612. The markup language document 614 identifies content andone or more instructions describing formatting or presentation of thecontent. By executing the instructions included in the markup languagedocument 614, the browser application 612 displays the identifiedcontent using the format or presentation described by the markuplanguage document 614. For example, the markup language document 614includes instructions for generating and displaying a web page havingmultiple frames that include text and/or image data retrieved from theexternal system 620 and the social networking system 630. In variousembodiments, the markup language document 614 comprises a data fileincluding extensible markup language (XML) data, extensible hypertextmarkup language (XHTML) data, or other markup language data.Additionally, the markup language document 614 may include JavaScriptObject Notation (JSON) data, JSON with padding (JSONP), and JavaScriptdata to facilitate data-interchange between the external system 620 andthe user device 610. The browser application 612 on the user device 610may use a JavaScript compiler to decode the markup language document614.

The markup language document 614 may also include, or link to,applications or application frameworks such as FLASH™ or Unity™applications, the Silverlight™ application framework, etc.

In one embodiment, the user device 610 also includes one or more cookies616 including data indicating whether a user of the user device 610 islogged into the social networking system 630, which may enablemodification of the data communicated from the social networking system630 to the user device 610.

The external system 620 includes one or more web servers that includeone or more web pages 622 a, 622 b, which are communicated to the userdevice 610 using the network 650. The external system 620 is separatefrom the social networking system 630. For example, the external system620 is associated with a first domain, while the social networkingsystem 630 is associated with a separate social networking domain. Webpages 622 a, 622 b, included in the external system 620, comprise markuplanguage documents 614 identifying content and including instructionsspecifying formatting or presentation of the identified content. Asdiscussed previously, it should be appreciated that there can be manyvariations or other possibilities.

The social networking system 630 includes one or more computing devicesfor a social network, including a plurality of users, and providingusers of the social network with the ability to communicate and interactwith other users of the social network. In some instances, the socialnetwork can be represented by a graph, i.e., a data structure includingedges and nodes. Other data structures can also be used to represent thesocial network, including but not limited to databases, objects,classes, meta elements, files, or any other data structure. The socialnetworking system 630 may be administered, managed, or controlled by anoperator. The operator of the social networking system 630 may be ahuman being, an automated application, or a series of applications formanaging content, regulating policies, and collecting usage metricswithin the social networking system 630. Any type of operator may beused.

Users may join the social networking system 630 and then add connectionsto any number of other users of the social networking system 630 to whomthey desire to be connected. As used herein, the term “friend” refers toany other user of the social networking system 630 to whom a user hasformed a connection, association, or relationship via the socialnetworking system 630. For example, in an embodiment, if users in thesocial networking system 630 are represented as nodes in the socialgraph, the term “friend” can refer to an edge formed between anddirectly connecting two user nodes.

Connections may be added explicitly by a user or may be automaticallycreated by the social networking system 630 based on commoncharacteristics of the users (e.g., users who are alumni of the sameeducational institution). For example, a first user specifically selectsa particular other user to be a friend. Connections in the socialnetworking system 630 are usually in both directions, but need not be,so the terms “user” and “friend” depend on the frame of reference.Connections between users of the social networking system 630 areusually bilateral (“two-way”), or “mutual,” but connections may also beunilateral, or “one-way.” For example, if Bob and Joe are both users ofthe social networking system 630 and connected to each other, Bob andJoe are each other's connections. If, on the other hand, Bob wishes toconnect to Joe to view data communicated to the social networking system630 by Joe, but Joe does not wish to form a mutual connection, aunilateral connection may be established. The connection between usersmay be a direct connection; however, some embodiments of the socialnetworking system 630 allow the connection to be indirect via one ormore levels of connections or degrees of separation.

In addition to establishing and maintaining connections between usersand allowing interactions between users, the social networking system630 provides users with the ability to take actions on various types ofitems supported by the social networking system 630. These items mayinclude groups or networks (i.e., social networks of people, entities,and concepts) to which users of the social networking system 630 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use via the socialnetworking system 630, transactions that allow users to buy or sellitems via services provided by or through the social networking system630, and interactions with advertisements that a user may perform on oroff the social networking system 630. These are just a few examples ofthe items upon which a user may act on the social networking system 630,and many others are possible. A user may interact with anything that iscapable of being represented in the social networking system 630 or inthe external system 620, separate from the social networking system 630,or coupled to the social networking system 630 via the network 650.

The social networking system 630 is also capable of linking a variety ofentities. For example, the social networking system 630 enables users tointeract with each other as well as external systems 620 or otherentities through an API, a web service, or other communication channels.The social networking system 630 generates and maintains the “socialgraph” comprising a plurality of nodes interconnected by a plurality ofedges. Each node in the social graph may represent an entity that canact on another node and/or that can be acted on by another node. Thesocial graph may include various types of nodes. Examples of types ofnodes include users, non-person entities, content items, web pages,groups, activities, messages, concepts, and any other things that can berepresented by an object in the social networking system 630. An edgebetween two nodes in the social graph may represent a particular kind ofconnection, or association, between the two nodes, which may result fromnode relationships or from an action that was performed by one of thenodes on the other node. In some cases, the edges between nodes can beweighted. The weight of an edge can represent an attribute associatedwith the edge, such as a strength of the connection or associationbetween nodes. Different types of edges can be provided with differentweights. For example, an edge created when one user “likes” another usermay be given one weight, while an edge created when a user befriendsanother user may be given a different weight.

As an example, when a first user identifies a second user as a friend,an edge in the social graph is generated connecting a node representingthe first user and a second node representing the second user. Asvarious nodes relate or interact with each other, the social networkingsystem 630 modifies edges connecting the various nodes to reflect therelationships and interactions.

The social networking system 630 also includes user-generated content,which enhances a user's interactions with the social networking system630. User-generated content may include anything a user can add, upload,send, or “post” to the social networking system 630. For example, a usercommunicates posts to the social networking system 630 from a userdevice 610. Posts may include data such as status updates or othertextual data, location information, images such as photos, videos,links, music or other similar data and/or media. Content may also beadded to the social networking system 630 by a third party. Content“items” are represented as objects in the social networking system 630.In this way, users of the social networking system 630 are encouraged tocommunicate with each other by posting text and content items of varioustypes of media through various communication channels. Suchcommunication increases the interaction of users with each other andincreases the frequency with which users interact with the socialnetworking system 630.

The social networking system 630 includes a web server 632, an APIrequest server 634, a user profile store 636, a connection store 638, anaction logger 640, an activity log 642, and an authorization server 644.In an embodiment of the invention, the social networking system 630 mayinclude additional, fewer, or different components for variousapplications. Other components, such as network interfaces, securitymechanisms, load balancers, failover servers, management and networkoperations consoles, and the like are not shown so as to not obscure thedetails of the system.

The user profile store 636 maintains information about user accounts,including biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, location, and the like that has been declared by users orinferred by the social networking system 630. This information is storedin the user profile store 636 such that each user is uniquelyidentified. The social networking system 630 also stores data describingone or more connections between different users in the connection store638. The connection information may indicate users who have similar orcommon work experience, group memberships, hobbies, or educationalhistory. Additionally, the social networking system 630 includesuser-defined connections between different users, allowing users tospecify their relationships with other users. For example, user-definedconnections allow users to generate relationships with other users thatparallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. Users may select from predefinedtypes of connections, or define their own connection types as needed.Connections with other nodes in the social networking system 630, suchas non-person entities, buckets, cluster centers, images, interests,pages, external systems, concepts, and the like are also stored in theconnection store 638.

The social networking system 630 maintains data about objects with whicha user may interact. To maintain this data, the user profile store 636and the connection store 638 store instances of the corresponding typeof objects maintained by the social networking system 630. Each objecttype has information fields that are suitable for storing informationappropriate to the type of object. For example, the user profile store636 contains data structures with fields suitable for describing auser's account and information related to a user's account. When a newobject of a particular type is created, the social networking system 630initializes a new data structure of the corresponding type, assigns aunique object identifier to it, and begins to add data to the object asneeded. This might occur, for example, when a user becomes a user of thesocial networking system 630, the social networking system 630 generatesa new instance of a user profile in the user profile store 636, assignsa unique identifier to the user account, and begins to populate thefields of the user account with information provided by the user.

The connection store 638 includes data structures suitable fordescribing a user's connections to other users, connections to externalsystems 620 or connections to other entities. The connection store 638may also associate a connection type with a user's connections, whichmay be used in conjunction with the user's privacy setting to regulateaccess to information about the user. In an embodiment of the invention,the user profile store 636 and the connection store 638 may beimplemented as a federated database.

Data stored in the connection store 638, the user profile store 636, andthe activity log 642 enables the social networking system 630 togenerate the social graph that uses nodes to identify various objectsand edges connecting nodes to identify relationships between differentobjects. For example, if a first user establishes a connection with asecond user in the social networking system 630, user accounts of thefirst user and the second user from the user profile store 636 may actas nodes in the social graph. The connection between the first user andthe second user stored by the connection store 638 is an edge betweenthe nodes associated with the first user and the second user. Continuingthis example, the second user may then send the first user a messagewithin the social networking system 630. The action of sending themessage, which may be stored, is another edge between the two nodes inthe social graph representing the first user and the second user.Additionally, the message itself may be identified and included in thesocial graph as another node connected to the nodes representing thefirst user and the second user.

In another example, a first user may tag a second user in an image thatis maintained by the social networking system 630 (or, alternatively, inan image maintained by another system outside of the social networkingsystem 630). The image may itself be represented as a node in the socialnetworking system 630. This tagging action may create edges between thefirst user and the second user as well as create an edge between each ofthe users and the image, which is also a node in the social graph. Inyet another example, if a user confirms attending an event, the user andthe event are nodes obtained from the user profile store 636, where theattendance of the event is an edge between the nodes that may beretrieved from the activity log 642. By generating and maintaining thesocial graph, the social networking system 630 includes data describingmany different types of objects and the interactions and connectionsamong those objects, providing a rich source of socially relevantinformation.

The web server 632 links the social networking system 630 to one or moreuser devices 610 and/or one or more external systems 620 via the network650. The web server 632 serves web pages, as well as other web-relatedcontent, such as Java, JavaScript, Flash, XML, and so forth. The webserver 632 may include a mail server or other messaging functionalityfor receiving and routing messages between the social networking system630 and one or more user devices 610. The messages can be instantmessages, queued messages (e.g., email), text and SMS messages, or anyother suitable messaging format.

The API request server 634 allows one or more external systems 620 anduser devices 610 to call access information from the social networkingsystem 630 by calling one or more API functions. The API request server634 may also allow external systems 620 to send information to thesocial networking system 630 by calling APIs. The external system 620,in one embodiment, sends an API request to the social networking system630 via the network 650, and the API request server 634 receives the APIrequest. The API request server 634 processes the request by calling anAPI associated with the API request to generate an appropriate response,which the API request server 634 communicates to the external system 620via the network 650. For example, responsive to an API request, the APIrequest server 634 collects data associated with a user, such as theuser's connections that have logged into the external system 620, andcommunicates the collected data to the external system 620. In anotherembodiment, the user device 610 communicates with the social networkingsystem 630 via APIs in the same manner as external systems 620.

The action logger 640 is capable of receiving communications from theweb server 632 about user actions on and/or off the social networkingsystem 630. The action logger 640 populates the activity log 642 withinformation about user actions, enabling the social networking system630 to discover various actions taken by its users within the socialnetworking system 630 and outside of the social networking system 630.Any action that a particular user takes with respect to another node onthe social networking system 630 may be associated with each user'saccount, through information maintained in the activity log 642 or in asimilar database or other data repository. Examples of actions taken bya user within the social networking system 630 that are identified andstored may include, for example, adding a connection to another user,sending a message to another user, reading a message from another user,viewing content associated with another user, attending an event postedby another user, posting an image, attempting to post an image, or otheractions interacting with another user or another object. When a usertakes an action within the social networking system 630, the action isrecorded in the activity log 642. In one embodiment, the socialnetworking system 630 maintains the activity log 642 as a database ofentries. When an action is taken within the social networking system630, an entry for the action is added to the activity log 642. Theactivity log 642 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actionsthat occur within an entity outside of the social networking system 630,such as an external system 620 that is separate from the socialnetworking system 630. For example, the action logger 640 may receivedata describing a user's interaction with an external system 620 fromthe web server 632. In this example, the external system 620 reports auser's interaction according to structured actions and objects in thesocial graph.

Other examples of actions where a user interacts with an external system620 include a user expressing an interest in an external system 620 oranother entity, a user posting a comment to the social networking system630 that discusses an external system 620 or a web page 622 a within theexternal system 620, a user posting to the social networking system 630a Uniform Resource Locator (URL) or other identifier associated with anexternal system 620, a user attending an event associated with anexternal system 620, or any other action by a user that is related to anexternal system 620. Thus, the activity log 642 may include actionsdescribing interactions between a user of the social networking system630 and an external system 620 that is separate from the socialnetworking system 630.

The authorization server 644 enforces one or more privacy settings ofthe users of the social networking system 630. A privacy setting of auser determines how particular information associated with a user can beshared. The privacy setting comprises the specification of particularinformation associated with a user and the specification of the entityor entities with whom the information can be shared. Examples ofentities with which information can be shared may include other users,applications, external systems 620, or any entity that can potentiallyaccess the information. The information that can be shared by a usercomprises user account information, such as profile photos, phonenumbers associated with the user, user's connections, actions taken bythe user such as adding a connection, changing user profile information,and the like.

The privacy setting specification may be provided at different levels ofgranularity. For example, the privacy setting may identify specificinformation to be shared with other users; the privacy settingidentifies a work phone number or a specific set of related information,such as, personal information including profile photo, home phonenumber, and status. Alternatively, the privacy setting may apply to allthe information associated with the user. The specification of the setof entities that can access particular information can also be specifiedat various levels of granularity. Various sets of entities with whichinformation can be shared may include, for example, all friends of theuser, all friends of friends, all applications, or all external systems620. One embodiment allows the specification of the set of entities tocomprise an enumeration of entities. For example, the user may provide alist of external systems 620 that are allowed to access certaininformation. Another embodiment allows the specification to comprise aset of entities along with exceptions that are not allowed to access theinformation. For example, a user may allow all external systems 620 toaccess the user's work information, but specify a list of externalsystems 620 that are not allowed to access the work information. Certainembodiments call the list of exceptions that are not allowed to accesscertain information a “block list”. External systems 620 belonging to ablock list specified by a user are blocked from accessing theinformation specified in the privacy setting. Various combinations ofgranularity of specification of information, and granularity ofspecification of entities, with which information is shared arepossible. For example, all personal information may be shared withfriends whereas all work information may be shared with friends offriends.

The authorization server 644 contains logic to determine if certaininformation associated with a user can be accessed by a user's friends,external systems 620, and/or other applications and entities. Theexternal system 620 may need authorization from the authorization server644 to access the user's more private and sensitive information, such asthe user's work phone number. Based on the user's privacy settings, theauthorization server 644 determines if another user, the external system620, an application, or another entity is allowed to access informationassociated with the user, including information about actions taken bythe user.

In some embodiments, the social networking system 630 can include acontent provider module 646. The content provider module 646 can, forexample, be implemented as the content provider module 102 of FIG. 1. Insome embodiments, the content provider module 646, or some of itsfeatures, can be implemented in a computing device, e.g., the userdevice 610. In some embodiments, the user device 610 can include aclient module 618. The client module 618 can, for example, beimplemented as the client module 114 of FIG. 1. The network 650 can, forexample, be implemented as the network 150 of FIG. 1. As discussedpreviously, it should be appreciated that there can be many variationsor other possibilities.

Hardware Implementation

The foregoing processes and features can be implemented by a widevariety of machine and computer system architectures and in a widevariety of network and computing environments. FIG. 7 illustrates anexample of a computer system 700 that may be used to implement one ormore of the embodiments described herein in accordance with anembodiment of the invention. The computer system 700 includes sets ofinstructions for causing the computer system 700 to perform theprocesses and features discussed herein. The computer system 700 may beconnected (e.g., networked) to other machines. In a networkeddeployment, the computer system 700 may operate in the capacity of aserver machine or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In an embodiment of the invention, the computersystem 700 may be the social networking system 630, the user device 610,and the external system 720, or a component thereof. In an embodiment ofthe invention, the computer system 700 may be one server among many thatconstitutes all or part of the social networking system 630.

The computer system 700 includes a processor 702, a cache 704, and oneor more executable modules and drivers, stored on a computer-readablemedium, directed to the processes and features described herein.Additionally, the computer system 700 includes a high performanceinput/output (I/O) bus 706 and a standard I/O bus 708. A host bridge 710couples processor 702 to high performance I/O bus 706, whereas I/O busbridge 712 couples the two buses 706 and 708 to each other. A systemmemory 714 and one or more network interfaces 716 couple to highperformance I/O bus 706. The computer system 700 may further includevideo memory and a display device coupled to the video memory (notshown). Mass storage 718 and I/O ports 720 couple to the standard I/Obus 708. The computer system 700 may optionally include a keyboard andpointing device, a display device, or other input/output devices (notshown) coupled to the standard I/O bus 708. Collectively, these elementsare intended to represent a broad category of computer hardware systems,including but not limited to computer systems based on thex86-compatible processors manufactured by Intel Corporation of SantaClara, Calif., and the x86-compatible processors manufactured byAdvanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well as anyother suitable processor.

An operating system manages and controls the operation of the computersystem 700, including the input and output of data to and from softwareapplications (not shown). The operating system provides an interfacebetween the software applications being executed on the system and thehardware components of the system. Any suitable operating system may beused, such as the LINUX Operating System, the Apple Macintosh OperatingSystem, available from Apple Computer Inc. of Cupertino, Calif., UNIXoperating systems, Microsoft® Windows® operating systems, BSD operatingsystems, and the like. Other implementations are possible.

The elements of the computer system 700 are described in greater detailbelow. In particular, the network interface 716 provides communicationbetween the computer system 700 and any of a wide range of networks,such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Themass storage 718 provides permanent storage for the data and programminginstructions to perform the above-described processes and featuresimplemented by the respective computing systems identified above,whereas the system memory 714 (e.g., DRAM) provides temporary storagefor the data and programming instructions when executed by the processor702. The I/O ports 720 may be one or more serial and/or parallelcommunication ports that provide communication between additionalperipheral devices, which may be coupled to the computer system 700.

The computer system 700 may include a variety of system architectures,and various components of the computer system 700 may be rearranged. Forexample, the cache 704 may be on-chip with processor 702. Alternatively,the cache 704 and the processor 702 may be packed together as a“processor module”, with processor 702 being referred to as the“processor core”. Furthermore, certain embodiments of the invention mayneither require nor include all of the above components. For example,peripheral devices coupled to the standard I/O bus 708 may couple to thehigh performance I/O bus 706. In addition, in some embodiments, only asingle bus may exist, with the components of the computer system 700being coupled to the single bus. Moreover, the computer system 700 mayinclude additional components, such as additional processors, storagedevices, or memories.

In general, the processes and features described herein may beimplemented as part of an operating system or a specific application,component, program, object, module, or series of instructions referredto as “programs”. For example, one or more programs may be used toexecute specific processes described herein. The programs typicallycomprise one or more instructions in various memory and storage devicesin the computer system 700 that, when read and executed by one or moreprocessors, cause the computer system 700 to perform operations toexecute the processes and features described herein. The processes andfeatures described herein may be implemented in software, firmware,hardware (e.g., an application specific integrated circuit), or anycombination thereof.

In one implementation, the processes and features described herein areimplemented as a series of executable modules run by the computer system700, individually or collectively in a distributed computingenvironment. The foregoing modules may be realized by hardware,executable modules stored on a computer-readable medium (ormachine-readable medium), or a combination of both. For example, themodules may comprise a plurality or series of instructions to beexecuted by a processor in a hardware system, such as the processor 702.Initially, the series of instructions may be stored on a storage device,such as the mass storage 718. However, the series of instructions can bestored on any suitable computer readable storage medium. Furthermore,the series of instructions need not be stored locally, and could bereceived from a remote storage device, such as a server on a network,via the network interface 716. The instructions are copied from thestorage device, such as the mass storage 718, into the system memory 714and then accessed and executed by the processor 702. In variousimplementations, a module or modules can be executed by a processor ormultiple processors in one or multiple locations, such as multipleservers in a parallel processing environment.

Examples of computer-readable media include, but are not limited to,recordable type media such as volatile and non-volatile memory devices;solid state memories; floppy and other removable disks; hard diskdrives; magnetic media; optical disks (e.g., Compact Disk Read-OnlyMemory (CD ROMS), Digital Versatile Disks (DVDs)); other similarnon-transitory (or transitory), tangible (or non-tangible) storagemedium; or any type of medium suitable for storing, encoding, orcarrying a series of instructions for execution by the computer system700 to perform any one or more of the processes and features describedherein.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the description.In other instances, functional block diagrams and flow diagrams areshown to represent data and logic flows. The components of blockdiagrams and flow diagrams (e.g., modules, blocks, structures, devices,features, etc.) may be variously combined, separated, removed,reordered, and replaced in a manner other than as expressly describedand depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “one series of embodiments”, “some embodiments”,“various embodiments”, or the like means that a particular feature,design, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of, for example, the phrase “in one embodiment” or “in anembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, whetheror not there is express reference to an “embodiment” or the like,various features are described, which may be variously combined andincluded in some embodiments, but also variously omitted in otherembodiments. Similarly, various features are described that may bepreferences or requirements for some embodiments, but not otherembodiments.

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:generating, by a computing system, a saliency prediction model foridentifying salient points of interest that appear during presentationof content items; providing, by the computing system, at least one frameof a content item to the saliency prediction model; obtaining, by thecomputing system, information describing at least a first salient pointof interest that appears in the at least one frame from the saliencyprediction model, wherein the first salient point of interest ispredicted to be of interest to one or more users accessing the contentitem; determining, by the computing system, a second content item basedat least in part on the first salient point of interest that appears inthe at least one frame; and causing, by the computing system, the secondcontent item to be presented after presentation of the content item. 2.The computer-implemented method of claim 1, wherein generating thesaliency prediction model further comprises: obtaining, by the computingsystem, a set of training content items; generating, by the computingsystem, respective aggregated heat map data for each of the trainingcontent items, wherein the aggregated heat map data for a trainingcontent item measures user view activity during presentation of thetraining content item; and training, by the computing system, thesaliency prediction model based at least in part on the set of trainingcontent items and the respective aggregated heat map data.
 3. Thecomputer-implemented method of claim 2, wherein generating respectiveaggregated heat map data for each of the training content items furthercomprises: obtaining, by the computing system, respective view trackingdata for a set of users that accessed a training content item, whereinthe view tracking data for a user identifies one or more regions thatwere viewed in frames by the user during presentation of the trainingcontent item; generating, by the computing system, user-specific heatmap data for each user in the set based at least in part on therespective view tracking data for the user; and aggregating, by thecomputing system, the user-specific heat map data to generate theaggregated heat map data for the training content item.
 4. Thecomputer-implemented method of claim 3, wherein the view tracking datafor a user is determined based at least in part on changes to the user'sviewport during presentation of the training content item.
 5. Thecomputer-implemented method of claim 4, wherein the changes to theviewport are determined based at least in part on sensor data thatdescribes movement of a computing device being used to present thetraining content item, gesture data that describes gestures performedduring presentation of the training content item, input device data thatdescribes input operations performed during presentation of the trainingcontent item, headset movement data that describes changes in theviewport direction during presentation of the training content item, oreye tracking data collected during presentation of the training contentitem.
 6. The computer-implemented method of claim 1, the method furthercomprising: determining, by the computing system, changes to a user'sviewport during presentation of the content item based at least in parton the information describing the first salient point of interest thatappears in the at least one frame.
 7. The computer-implemented method ofclaim 1, the method further comprising: causing, by the computingsystem, a first region that corresponds to the first salient point ofinterest in the at least one frame to be allocated a greater number ofbits than a second region in the frame that does not correspond to asalient point of interest, wherein the first region is presented at ahigher video quality than the second region during presentation of thecontent item.
 8. The computer-implemented method of claim 1, the methodfurther comprising: generating, by the computing system, an automatedguide for the content item, wherein the automated guide causes userviewports to automatically transition between salient points of interestthat appear during presentation of the content item.
 9. Thecomputer-implemented method of claim 1, wherein determining the secondcontent item further comprises: determining, by the computing system, ascore for the first salient point of interest based at least in part onan amount of user view activity corresponding to the first salient pointof interest; determining, by the computing system, a score for a secondsalient point of interest that also appears in the content item based atleast in part on an amount of user view activity corresponding to thesecond salient point of interest, wherein the score for the firstsalient point of interest is greater than the score for the secondsalient point of interest; and determining, by the computing system, thesecond content item based at least in part on the first salient point ofinterest.
 10. A system comprising: at least one processor; and a memorystoring instructions that, when executed by the at least one processor,cause the system to perform: generating a saliency prediction model foridentifying salient points of interest that appear during presentationof content items; providing at least one frame of a content item to thesaliency prediction model; obtaining information describing at least afirst salient point of interest that appears in the at least one framefrom the saliency prediction model, wherein the first salient point ofinterest is predicted to be of interest to one or more users accessingthe content item; determining a second content item based at least inpart on the first salient point of interest that appears in the at leastone frame; and causing the second content item to be presented afterpresentation of the content item.
 11. The system of claim 10, whereingenerating the saliency prediction model further causes the system toperform: obtaining a set of training content items; generatingrespective aggregated heat map data for each of the training contentitems, wherein the aggregated heat map data for a training content itemmeasures user view activity during presentation of the training contentitem; and training the saliency prediction model based at least in parton the set of training content items and the respective aggregated heatmap data.
 12. The system of claim 11, wherein generating respectiveaggregated heat map data for each of the training content items furthercauses the system to perform: obtaining respective view tracking datafor a set of users that accessed a training content item, wherein theview tracking data for a user identifies one or more regions that wereviewed in frames by the user during presentation of the training contentitem; generating user-specific heat map data for each user in the setbased at least in part on the respective view tracking data for theuser; and aggregating the user-specific heat map data to generate theaggregated heat map data for the training content item.
 13. The systemof claim 12, wherein the view tracking data for a user is determinedbased at least in part on changes to the user's viewport duringpresentation of the training content item.
 14. The system of claim 13,wherein the changes to the viewport are determined based at least inpart on sensor data that describes movement of a computing device beingused to present the training content item, gesture data that describesgestures performed during presentation of the training content item,input device data that describes input operations performed duringpresentation of the training content item, headset movement data thatdescribes changes in the viewport direction during presentation of thetraining content item, or eye tracking data collected duringpresentation of the training content item.
 15. A non-transitorycomputer-readable storage medium including instructions that, whenexecuted by at least one processor of a computing system, cause thecomputing system to perform a method comprising: generating a saliencyprediction model for identifying salient points of interest that appearduring presentation of content items; providing at least one frame of acontent item to the saliency prediction model; obtaining informationdescribing at least a first salient point of interest that appears inthe at least one frame from the saliency prediction model, wherein thefirst salient point of interest is predicted to be of interest to one ormore users accessing the content item; determining a second content itembased at least in part on the first salient point of interest thatappears in the at least one frame; and causing the second content itemto be presented after presentation of the content item.
 16. Thenon-transitory computer-readable storage medium of claim 15, whereingenerating the saliency prediction model further causes the computingsystem to perform: obtaining a set of training content items; generatingrespective aggregated heat map data for each of the training contentitems, wherein the aggregated heat map data for a training content itemmeasures user view activity during presentation of the training contentitem; and training the saliency prediction model based at least in parton the set of training content items and the respective aggregated heatmap data.
 17. The non-transitory computer-readable storage medium ofclaim 16, wherein generating respective aggregated heat map data foreach of the training content items further causes the computing systemto perform: obtaining respective view tracking data for a set of usersthat accessed a training content item, wherein the view tracking datafor a user identifies one or more regions that were viewed in frames bythe user during presentation of the training content item; generatinguser-specific heat map data for each user in the set based at least inpart on the respective view tracking data for the user; and aggregatingthe user-specific heat map data to generate the aggregated heat map datafor the training content item.
 18. The non-transitory computer-readablestorage medium of claim 17, wherein the view tracking data for a user isdetermined based at least in part on changes to the user's viewportduring presentation of the training content item.
 19. The non-transitorycomputer-readable storage medium of claim 18, wherein the changes to theviewport are determined based at least in part on sensor data thatdescribes movement of a computing device being used to present thetraining content item, gesture data that describes gestures performedduring presentation of the training content item, input device data thatdescribes input operations performed during presentation of the trainingcontent item, headset movement data that describes changes in theviewport direction during presentation of the training content item, oreye tracking data collected during presentation of the training contentitem.